Overview

Dataset statistics

Number of variables23
Number of observations4104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory709.6 KiB
Average record size in memory177.1 B

Variable types

Numeric22
Categorical1

Alerts

motor_UPDRS is highly correlated with total_UPDRSHigh correlation
total_UPDRS is highly correlated with motor_UPDRSHigh correlation
Jitter(%) is highly correlated with Jitter(Abs) and 13 other fieldsHigh correlation
Jitter(Abs) is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Jitter:RAP is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Jitter:PPQ5 is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer(dB) is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ11 is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Shimmer:DDA is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
NHR is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
HNR is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
RPDE is highly correlated with Jitter(%) and 6 other fieldsHigh correlation
PPE is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
motor_UPDRS is highly correlated with total_UPDRSHigh correlation
total_UPDRS is highly correlated with motor_UPDRSHigh correlation
Jitter(%) is highly correlated with Jitter(Abs) and 12 other fieldsHigh correlation
Jitter(Abs) is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Jitter:RAP is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Jitter:PPQ5 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Jitter:DDP is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer(dB) is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:APQ11 is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Shimmer:DDA is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
NHR is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
HNR is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
RPDE is highly correlated with Jitter(Abs) and 2 other fieldsHigh correlation
PPE is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
motor_UPDRS is highly correlated with total_UPDRSHigh correlation
total_UPDRS is highly correlated with motor_UPDRSHigh correlation
Jitter(%) is highly correlated with Jitter(Abs) and 6 other fieldsHigh correlation
Jitter(Abs) is highly correlated with Jitter(%) and 6 other fieldsHigh correlation
Jitter:RAP is highly correlated with Jitter(%) and 6 other fieldsHigh correlation
Jitter:PPQ5 is highly correlated with Jitter(%) and 8 other fieldsHigh correlation
Jitter:DDP is highly correlated with Jitter(%) and 6 other fieldsHigh correlation
Shimmer is highly correlated with Jitter:PPQ5 and 6 other fieldsHigh correlation
Shimmer(dB) is highly correlated with Jitter:PPQ5 and 7 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with Shimmer and 5 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with Shimmer and 5 other fieldsHigh correlation
Shimmer:APQ11 is highly correlated with Shimmer and 5 other fieldsHigh correlation
Shimmer:DDA is highly correlated with Shimmer and 5 other fieldsHigh correlation
NHR is highly correlated with Jitter(%) and 7 other fieldsHigh correlation
HNR is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
PPE is highly correlated with Jitter(%) and 6 other fieldsHigh correlation
df_index is highly correlated with subject# and 6 other fieldsHigh correlation
subject# is highly correlated with df_index and 6 other fieldsHigh correlation
age is highly correlated with df_index and 3 other fieldsHigh correlation
sex is highly correlated with df_index and 1 other fieldsHigh correlation
motor_UPDRS is highly correlated with df_index and 3 other fieldsHigh correlation
total_UPDRS is highly correlated with df_index and 3 other fieldsHigh correlation
Jitter(%) is highly correlated with Jitter(Abs) and 13 other fieldsHigh correlation
Jitter(Abs) is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
Jitter:RAP is highly correlated with Jitter(%) and 14 other fieldsHigh correlation
Jitter:PPQ5 is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Jitter:DDP is highly correlated with Jitter(%) and 14 other fieldsHigh correlation
Shimmer is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Shimmer(dB) is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Shimmer:APQ3 is highly correlated with Jitter(%) and 11 other fieldsHigh correlation
Shimmer:APQ5 is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
Shimmer:APQ11 is highly correlated with Jitter(%) and 11 other fieldsHigh correlation
Shimmer:DDA is highly correlated with Jitter(%) and 11 other fieldsHigh correlation
NHR is highly correlated with Jitter(%) and 13 other fieldsHigh correlation
HNR is highly correlated with df_index and 15 other fieldsHigh correlation
RPDE is highly correlated with Jitter(Abs) and 8 other fieldsHigh correlation
DFA is highly correlated with df_index and 6 other fieldsHigh correlation
PPE is highly correlated with Jitter(%) and 12 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2022-05-10 17:12:28.346312
Analysis finished2022-05-10 17:13:40.422482
Duration1 minute and 12.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct4104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2952.019737
Minimum0
Maximum5874
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:40.527506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile323.45
Q11505.25
median2958.5
Q34402.75
95-th percentile5569.85
Maximum5874
Range5874
Interquartile range (IQR)2897.5

Descriptive statistics

Standard deviation1681.704785
Coefficient of variation (CV)0.569679384
Kurtosis-1.189026929
Mean2952.019737
Median Absolute Deviation (MAD)1449
Skewness-0.003981580914
Sum12115089
Variance2828130.984
MonotonicityNot monotonic
2022-05-10T12:13:40.662939image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
45471
 
< 0.1%
47351
 
< 0.1%
6371
 
< 0.1%
26841
 
< 0.1%
47311
 
< 0.1%
26801
 
< 0.1%
47271
 
< 0.1%
6291
 
< 0.1%
26761
 
< 0.1%
Other values (4094)4094
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
58741
< 0.1%
58731
< 0.1%
58721
< 0.1%
58681
< 0.1%
58661
< 0.1%
58631
< 0.1%
58611
< 0.1%
58601
< 0.1%
58581
< 0.1%
58561
< 0.1%

subject#
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.59844055
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:40.804450image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile41
Maximum42
Range41
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.27190358
Coefficient of variation (CV)0.5681847052
Kurtosis-1.238306555
Mean21.59844055
Median Absolute Deviation (MAD)11
Skewness-0.001227455083
Sum88640
Variance150.5996174
MonotonicityNot monotonic
2022-05-10T12:13:40.929856image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41116
 
2.8%
24116
 
2.8%
7116
 
2.8%
35116
 
2.8%
29115
 
2.8%
38115
 
2.8%
5108
 
2.6%
9108
 
2.6%
34107
 
2.6%
10105
 
2.6%
Other values (32)2982
72.7%
ValueCountFrequency (%)
195
2.3%
293
2.3%
395
2.3%
496
2.3%
5108
2.6%
6102
2.5%
7116
2.8%
8105
2.6%
9108
2.6%
10105
2.6%
ValueCountFrequency (%)
4297
2.4%
41116
2.8%
4096
2.3%
39102
2.5%
38115
2.8%
37102
2.5%
3687
2.1%
35116
2.8%
34107
2.6%
3391
2.2%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.78411306
Minimum36
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:41.086509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile49
Q158
median65
Q372
95-th percentile78
Maximum85
Range49
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.822714632
Coefficient of variation (CV)0.1361863922
Kurtosis0.6712772643
Mean64.78411306
Median Absolute Deviation (MAD)7
Skewness-0.3962654495
Sum265874
Variance77.84029349
MonotonicityNot monotonic
2022-05-10T12:13:41.189666image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
66296
 
7.2%
65292
 
7.1%
58290
 
7.1%
73269
 
6.6%
57262
 
6.4%
68224
 
5.5%
72211
 
5.1%
67207
 
5.0%
59206
 
5.0%
75201
 
4.9%
Other values (13)1646
40.1%
ValueCountFrequency (%)
3671
 
1.7%
49186
4.5%
55188
4.6%
56102
 
2.5%
57262
6.4%
58290
7.1%
59206
5.0%
60116
 
2.8%
6197
 
2.4%
62163
4.0%
ValueCountFrequency (%)
8596
 
2.3%
78115
2.8%
7696
 
2.3%
75201
4.9%
74198
4.8%
73269
6.6%
72211
5.1%
71116
2.8%
68224
5.5%
67207
5.0%

sex
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
2791 
1
1313 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02791
68.0%
11313
32.0%

Length

2022-05-10T12:13:41.306459image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-10T12:13:41.384589image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02791
68.0%
11313
32.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

test_time
Real number (ℝ≥0)

Distinct2089
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.85115277
Minimum0.39653
Maximum215.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:41.463110image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.39653
5-th percentile13.306
Q147.36275
median91.397
Q3137.83
95-th percentile175.46
Maximum215.49
Range215.09347
Interquartile range (IQR)90.46725

Descriptive statistics

Standard deviation53.07275794
Coefficient of variation (CV)0.5715896503
Kurtosis-1.123403441
Mean92.85115277
Median Absolute Deviation (MAD)45.403
Skewness0.1023613355
Sum381061.131
Variance2816.717636
MonotonicityNot monotonic
2022-05-10T12:13:41.604722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.2611
 
0.3%
147.3410
 
0.2%
119.349
 
0.2%
132.418
 
0.2%
168.398
 
0.2%
136.88
 
0.2%
123.838
 
0.2%
105.48
 
0.2%
158.88
 
0.2%
119.357
 
0.2%
Other values (2079)4019
97.9%
ValueCountFrequency (%)
0.396531
< 0.1%
0.398612
< 0.1%
0.399311
< 0.1%
0.498612
< 0.1%
0.499311
< 0.1%
0.501391
< 0.1%
2.48611
< 0.1%
2.48682
< 0.1%
2.48891
< 0.1%
2.48961
< 0.1%
ValueCountFrequency (%)
215.495
0.1%
212.394
0.1%
208.536
0.1%
204.46
0.1%
202.436
0.1%
202.34
0.1%
201.534
0.1%
198.365
0.1%
196.365
0.1%
195.795
0.1%

motor_UPDRS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1046
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2342039
Minimum5.0377
Maximum39.511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:41.761385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum5.0377
5-th percentile8.4496
Q114.89
median20.652
Q327.54825
95-th percentile34.985
Maximum39.511
Range34.4733
Interquartile range (IQR)12.65825

Descriptive statistics

Standard deviation8.107803117
Coefficient of variation (CV)0.381827506
Kurtosis-0.936941548
Mean21.2342039
Median Absolute Deviation (MAD)6.342
Skewness0.07658536082
Sum87145.1728
Variance65.73647138
MonotonicityNot monotonic
2022-05-10T12:13:41.918442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1599
 
2.4%
1289
 
2.2%
3161
 
1.5%
1858
 
1.4%
1752
 
1.3%
651
 
1.2%
31.77610
 
0.2%
23.44710
 
0.2%
33.6439
 
0.2%
23.5419
 
0.2%
Other values (1036)3656
89.1%
ValueCountFrequency (%)
5.03772
 
< 0.1%
5.03782
 
< 0.1%
5.13752
 
< 0.1%
5.13762
 
< 0.1%
5.4371
 
< 0.1%
5.43713
0.1%
5.53736
0.1%
5.63725
0.1%
5.73772
 
< 0.1%
5.83715
0.1%
ValueCountFrequency (%)
39.5113
0.1%
37.9574
0.1%
37.6644
0.1%
37.6592
 
< 0.1%
37.6583
0.1%
37.3645
0.1%
37.2982
 
< 0.1%
37.2972
 
< 0.1%
37.2822
 
< 0.1%
37.2812
 
< 0.1%

total_UPDRS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1093
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.9466251
Minimum7
Maximum54.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:42.091546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12.319
Q121.343
median27.486
Q336.399
95-th percentile47.0667
Maximum54.992
Range47.992
Interquartile range (IQR)15.056

Descriptive statistics

Standard deviation10.65694893
Coefficient of variation (CV)0.3681585985
Kurtosis-0.3356411854
Mean28.9466251
Median Absolute Deviation (MAD)7.3
Skewness0.2668601491
Sum118796.9494
Variance113.5705605
MonotonicityNot monotonic
2022-05-10T12:13:42.385129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3273
 
1.8%
1950
 
1.2%
739
 
1.0%
26.47710
 
0.2%
40.6110
 
0.2%
27.4869
 
0.2%
26.6189
 
0.2%
26.2189
 
0.2%
35.9928
 
0.2%
34.0448
 
0.2%
Other values (1083)3879
94.5%
ValueCountFrequency (%)
739
1.0%
7.08811
 
< 0.1%
7.08821
 
< 0.1%
7.08831
 
< 0.1%
7.09826
 
0.1%
7.16981
 
< 0.1%
7.16994
 
0.1%
7.24091
 
< 0.1%
7.2413
 
0.1%
7.31196
 
0.1%
ValueCountFrequency (%)
54.9924
0.1%
54.9484
0.1%
54.8684
0.1%
54.7895
0.1%
54.7275
0.1%
54.7094
0.1%
54.633
0.1%
54.6133
0.1%
54.5566
0.1%
54.552
 
< 0.1%

Jitter(%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006153625731
Minimum0.00084
Maximum0.09999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:42.626688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00084
5-th percentile0.0023115
Q10.00357
median0.00493
Q30.00681
95-th percentile0.0134655
Maximum0.09999
Range0.09915
Interquartile range (IQR)0.00324

Descriptive statistics

Standard deviation0.005642107799
Coefficient of variation (CV)0.9168753586
Kurtosis80.09606184
Mean0.006153625731
Median Absolute Deviation (MAD)0.00154
Skewness6.983398662
Sum25.25448
Variance3.183338042 × 10-5
MonotonicityNot monotonic
2022-05-10T12:13:42.796725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0040917
 
0.4%
0.0034915
 
0.4%
0.0035814
 
0.3%
0.003214
 
0.3%
0.0042913
 
0.3%
0.0043213
 
0.3%
0.0037313
 
0.3%
0.0046513
 
0.3%
0.0048213
 
0.3%
0.0041913
 
0.3%
Other values (1145)3966
96.6%
ValueCountFrequency (%)
0.000841
< 0.1%
0.000851
< 0.1%
0.00091
< 0.1%
0.001061
< 0.1%
0.001091
< 0.1%
0.00121
< 0.1%
0.001211
< 0.1%
0.001271
< 0.1%
0.001311
< 0.1%
0.001332
< 0.1%
ValueCountFrequency (%)
0.099991
< 0.1%
0.099621
< 0.1%
0.089291
< 0.1%
0.085891
< 0.1%
0.080341
< 0.1%
0.067141
< 0.1%
0.058931
< 0.1%
0.051571
< 0.1%
0.049181
< 0.1%
0.047671
< 0.1%

Jitter(Abs)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3152
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.410895224 × 10-5
Minimum2.25 × 10-6
Maximum0.0003957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:43.031125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.25 × 10-6
5-th percentile1.19315 × 10-5
Q12.2275 × 10-5
median3.485 × 10-5
Q35.39875 × 10-5
95-th percentile0.000107995
Maximum0.0003957
Range0.00039345
Interquartile range (IQR)3.17125 × 10-5

Descriptive statistics

Standard deviation3.530223909 × 10-5
Coefficient of variation (CV)0.8003418195
Kurtosis14.66720577
Mean4.410895224 × 10-5
Median Absolute Deviation (MAD)1.462 × 10-5
Skewness2.978668016
Sum0.18102314
Variance1.246248085 × 10-9
MonotonicityNot monotonic
2022-05-10T12:13:43.195161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.351 × 10-56
 
0.1%
3.101 × 10-56
 
0.1%
2.095 × 10-56
 
0.1%
2.657 × 10-55
 
0.1%
1.744 × 10-55
 
0.1%
3.032 × 10-55
 
0.1%
2.373 × 10-55
 
0.1%
4.169 × 10-54
 
0.1%
2.027 × 10-54
 
0.1%
2.055 × 10-54
 
0.1%
Other values (3142)4054
98.8%
ValueCountFrequency (%)
2.25 × 10-61
< 0.1%
3.61 × 10-61
< 0.1%
3.78 × 10-61
< 0.1%
3.79 × 10-61
< 0.1%
3.94 × 10-61
< 0.1%
4.01 × 10-61
< 0.1%
4.34 × 10-61
< 0.1%
4.54 × 10-61
< 0.1%
5.41 × 10-61
< 0.1%
5.44 × 10-61
< 0.1%
ValueCountFrequency (%)
0.00039571
< 0.1%
0.000390891
< 0.1%
0.000337711
< 0.1%
0.000321971
< 0.1%
0.000305531
< 0.1%
0.000290071
< 0.1%
0.000286481
< 0.1%
0.000284631
< 0.1%
0.000276971
< 0.1%
0.000259861
< 0.1%

Jitter:RAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct756
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002988209064
Minimum0.00033
Maximum0.05754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:43.355197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00033
5-th percentile0.00098
Q10.00157
median0.00226
Q30.00331
95-th percentile0.00706
Maximum0.05754
Range0.05721
Interquartile range (IQR)0.00174

Descriptive statistics

Standard deviation0.003136258653
Coefficient of variation (CV)1.049544588
Kurtosis92.07217074
Mean0.002988209064
Median Absolute Deviation (MAD)0.00079
Skewness7.492634115
Sum12.26361
Variance9.836118337 × 10-6
MonotonicityNot monotonic
2022-05-10T12:13:43.535237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0019328
 
0.7%
0.0011928
 
0.7%
0.0020823
 
0.6%
0.0016923
 
0.6%
0.0014922
 
0.5%
0.0025821
 
0.5%
0.0012421
 
0.5%
0.001221
 
0.5%
0.0020120
 
0.5%
0.0017620
 
0.5%
Other values (746)3877
94.5%
ValueCountFrequency (%)
0.000331
< 0.1%
0.00042
< 0.1%
0.000421
< 0.1%
0.000431
< 0.1%
0.000461
< 0.1%
0.000481
< 0.1%
0.000491
< 0.1%
0.000522
< 0.1%
0.000531
< 0.1%
0.000551
< 0.1%
ValueCountFrequency (%)
0.057541
< 0.1%
0.054511
< 0.1%
0.054351
< 0.1%
0.049611
< 0.1%
0.045221
< 0.1%
0.039791
< 0.1%
0.029771
< 0.1%
0.029411
< 0.1%
0.0281
< 0.1%
0.025991
< 0.1%

Jitter:PPQ5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct743
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003278745127
Minimum0.00045
Maximum0.06956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:43.749285image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00045
5-th percentile0.00117
Q10.00182
median0.00249
Q30.00349
95-th percentile0.0069585
Maximum0.06956
Range0.06911
Interquartile range (IQR)0.00167

Descriptive statistics

Standard deviation0.003743512098
Coefficient of variation (CV)1.141751479
Kurtosis93.39218428
Mean0.003278745127
Median Absolute Deviation (MAD)0.0008
Skewness8.074229772
Sum13.45597
Variance1.401388283 × 10-5
MonotonicityNot monotonic
2022-05-10T12:13:43.886317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0016723
 
0.6%
0.0025322
 
0.5%
0.0021622
 
0.5%
0.002222
 
0.5%
0.0019122
 
0.5%
0.0018522
 
0.5%
0.0017522
 
0.5%
0.0021421
 
0.5%
0.002421
 
0.5%
0.0019421
 
0.5%
Other values (733)3886
94.7%
ValueCountFrequency (%)
0.000452
< 0.1%
0.000522
< 0.1%
0.000561
 
< 0.1%
0.000581
 
< 0.1%
0.000633
0.1%
0.000644
0.1%
0.000651
 
< 0.1%
0.000681
 
< 0.1%
0.000692
< 0.1%
0.00071
 
< 0.1%
ValueCountFrequency (%)
0.069561
< 0.1%
0.060841
< 0.1%
0.057241
< 0.1%
0.056891
< 0.1%
0.055131
< 0.1%
0.049171
< 0.1%
0.043411
< 0.1%
0.039931
< 0.1%
0.036571
< 0.1%
0.035561
< 0.1%

Jitter:DDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1502
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008964812378
Minimum0.00098
Maximum0.17263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:44.038350image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00098
5-th percentile0.00295
Q10.00472
median0.00678
Q30.00992
95-th percentile0.0211785
Maximum0.17263
Range0.17165
Interquartile range (IQR)0.0052

Descriptive statistics

Standard deviation0.009408756598
Coefficient of variation (CV)1.049520749
Kurtosis92.06985648
Mean0.008964812378
Median Absolute Deviation (MAD)0.00238
Skewness7.492504207
Sum36.79159
Variance8.852470072 × 10-5
MonotonicityNot monotonic
2022-05-10T12:13:44.170393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0057814
 
0.3%
0.0035812
 
0.3%
0.0050812
 
0.3%
0.0035611
 
0.3%
0.0066711
 
0.3%
0.0049911
 
0.3%
0.005811
 
0.3%
0.005911
 
0.3%
0.0062511
 
0.3%
0.0044210
 
0.2%
Other values (1492)3990
97.2%
ValueCountFrequency (%)
0.000981
< 0.1%
0.001212
< 0.1%
0.001251
< 0.1%
0.001281
< 0.1%
0.001381
< 0.1%
0.001431
< 0.1%
0.001471
< 0.1%
0.001562
< 0.1%
0.001581
< 0.1%
0.001641
< 0.1%
ValueCountFrequency (%)
0.172631
< 0.1%
0.163521
< 0.1%
0.163041
< 0.1%
0.148831
< 0.1%
0.135671
< 0.1%
0.119371
< 0.1%
0.089321
< 0.1%
0.088221
< 0.1%
0.083991
< 0.1%
0.077961
< 0.1%

Shimmer
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2845
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03403431774
Minimum0.00306
Maximum0.23915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:44.318413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00306
5-th percentile0.0120015
Q10.01913
median0.02743
Q30.03987
95-th percentile0.0742415
Maximum0.23915
Range0.23609
Interquartile range (IQR)0.02074

Descriptive statistics

Standard deviation0.02577599947
Coefficient of variation (CV)0.7573532005
Kurtosis14.56641167
Mean0.03403431774
Median Absolute Deviation (MAD)0.00959
Skewness3.25456515
Sum139.67684
Variance0.0006644021485
MonotonicityNot monotonic
2022-05-10T12:13:44.461446image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018076
 
0.1%
0.017846
 
0.1%
0.019726
 
0.1%
0.026255
 
0.1%
0.015195
 
0.1%
0.014535
 
0.1%
0.016825
 
0.1%
0.028375
 
0.1%
0.024545
 
0.1%
0.015025
 
0.1%
Other values (2835)4051
98.7%
ValueCountFrequency (%)
0.003061
< 0.1%
0.003441
< 0.1%
0.003791
< 0.1%
0.004061
< 0.1%
0.004551
< 0.1%
0.004831
< 0.1%
0.004941
< 0.1%
0.005041
< 0.1%
0.005161
< 0.1%
0.005491
< 0.1%
ValueCountFrequency (%)
0.239151
< 0.1%
0.226911
< 0.1%
0.222211
< 0.1%
0.218051
< 0.1%
0.213621
< 0.1%
0.204061
< 0.1%
0.201751
< 0.1%
0.201261
< 0.1%
0.197741
< 0.1%
0.195821
< 0.1%

Shimmer(dB)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct770
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3109373782
Minimum0.026
Maximum1.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:44.605345image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.026
5-th percentile0.11015
Q10.175
median0.253
Q30.36625
95-th percentile0.672
Maximum1.97
Range1.944
Interquartile range (IQR)0.19125

Descriptive statistics

Standard deviation0.2297642111
Coefficient of variation (CV)0.738940466
Kurtosis12.704622
Mean0.3109373782
Median Absolute Deviation (MAD)0.089
Skewness3.055599958
Sum1276.087
Variance0.05279159271
MonotonicityNot monotonic
2022-05-10T12:13:44.760386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16425
 
0.6%
0.17822
 
0.5%
0.20822
 
0.5%
0.20121
 
0.5%
0.1721
 
0.5%
0.20620
 
0.5%
0.1619
 
0.5%
0.17219
 
0.5%
0.14219
 
0.5%
0.22219
 
0.5%
Other values (760)3897
95.0%
ValueCountFrequency (%)
0.0261
< 0.1%
0.031
< 0.1%
0.0341
< 0.1%
0.0351
< 0.1%
0.0411
< 0.1%
0.0451
< 0.1%
0.0471
< 0.1%
0.0491
< 0.1%
0.051
< 0.1%
0.0511
< 0.1%
ValueCountFrequency (%)
1.971
< 0.1%
1.9371
< 0.1%
1.8721
< 0.1%
1.8451
< 0.1%
1.8281
< 0.1%
1.7981
< 0.1%
1.7521
< 0.1%
1.7221
< 0.1%
1.7161
< 0.1%
1.711
< 0.1%

Shimmer:APQ3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2238
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01715053606
Minimum0.00161
Maximum0.16267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:44.921416image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00161
5-th percentile0.0054
Q10.0092675
median0.0137
Q30.02061
95-th percentile0.0392465
Maximum0.16267
Range0.16106
Interquartile range (IQR)0.0113425

Descriptive statistics

Standard deviation0.01322787125
Coefficient of variation (CV)0.7712803377
Kurtosis15.34006771
Mean0.01715053606
Median Absolute Deviation (MAD)0.00519
Skewness3.121533445
Sum70.3858
Variance0.0001749765777
MonotonicityNot monotonic
2022-05-10T12:13:45.066874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01110
 
0.2%
0.012298
 
0.2%
0.007978
 
0.2%
0.011097
 
0.2%
0.010647
 
0.2%
0.008637
 
0.2%
0.008176
 
0.1%
0.017616
 
0.1%
0.009696
 
0.1%
0.010476
 
0.1%
Other values (2228)4033
98.3%
ValueCountFrequency (%)
0.001611
< 0.1%
0.001721
< 0.1%
0.001781
< 0.1%
0.001851
< 0.1%
0.001931
< 0.1%
0.002051
< 0.1%
0.002111
< 0.1%
0.002141
< 0.1%
0.002171
< 0.1%
0.002211
< 0.1%
ValueCountFrequency (%)
0.162671
< 0.1%
0.144281
< 0.1%
0.107051
< 0.1%
0.105741
< 0.1%
0.102951
< 0.1%
0.102911
< 0.1%
0.100981
< 0.1%
0.100511
< 0.1%
0.100311
< 0.1%
0.09831
< 0.1%

Shimmer:APQ5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2390
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02010267057
Minimum0.00194
Maximum0.16702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:45.220918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00194
5-th percentile0.0065215
Q10.01078
median0.015895
Q30.02381
95-th percentile0.0448955
Maximum0.16702
Range0.16508
Interquartile range (IQR)0.01303

Descriptive statistics

Standard deviation0.01646299921
Coefficient of variation (CV)0.8189458788
Kurtosis18.70243678
Mean0.02010267057
Median Absolute Deviation (MAD)0.005965
Skewness3.627209753
Sum82.50136
Variance0.0002710303431
MonotonicityNot monotonic
2022-05-10T12:13:45.347956image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012148
 
0.2%
0.014527
 
0.2%
0.01547
 
0.2%
0.011757
 
0.2%
0.01117
 
0.2%
0.014626
 
0.1%
0.019776
 
0.1%
0.009456
 
0.1%
0.009626
 
0.1%
0.00856
 
0.1%
Other values (2380)4038
98.4%
ValueCountFrequency (%)
0.001941
< 0.1%
0.002051
< 0.1%
0.002221
< 0.1%
0.002331
< 0.1%
0.002351
< 0.1%
0.002381
< 0.1%
0.002531
< 0.1%
0.002581
< 0.1%
0.002651
< 0.1%
0.002661
< 0.1%
ValueCountFrequency (%)
0.167021
< 0.1%
0.162461
< 0.1%
0.152791
< 0.1%
0.152121
< 0.1%
0.15171
< 0.1%
0.147791
< 0.1%
0.142811
< 0.1%
0.138331
< 0.1%
0.133021
< 0.1%
0.132741
< 0.1%

Shimmer:APQ11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2669
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02743364522
Minimum0.00249
Maximum0.27546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:45.491983image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00249
5-th percentile0.0094915
Q10.0156475
median0.022525
Q30.0329325
95-th percentile0.06083
Maximum0.27546
Range0.27297
Interquartile range (IQR)0.017285

Descriptive statistics

Standard deviation0.01997780153
Coefficient of variation (CV)0.7282226393
Kurtosis19.23640334
Mean0.02743364522
Median Absolute Deviation (MAD)0.008095
Skewness3.393241657
Sum112.58768
Variance0.0003991125541
MonotonicityNot monotonic
2022-05-10T12:13:45.613010image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01568
 
0.2%
0.03136
 
0.1%
0.014256
 
0.1%
0.011816
 
0.1%
0.025096
 
0.1%
0.019155
 
0.1%
0.011115
 
0.1%
0.011395
 
0.1%
0.039065
 
0.1%
0.017495
 
0.1%
Other values (2659)4047
98.6%
ValueCountFrequency (%)
0.002491
< 0.1%
0.002921
< 0.1%
0.002991
< 0.1%
0.003181
< 0.1%
0.00322
< 0.1%
0.003461
< 0.1%
0.003751
< 0.1%
0.003781
< 0.1%
0.003861
< 0.1%
0.004031
< 0.1%
ValueCountFrequency (%)
0.275461
< 0.1%
0.205851
< 0.1%
0.186361
< 0.1%
0.179151
< 0.1%
0.176681
< 0.1%
0.176111
< 0.1%
0.174111
< 0.1%
0.16861
< 0.1%
0.163281
< 0.1%
0.161841
< 0.1%

Shimmer:DDA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3216
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05145137671
Minimum0.00484
Maximum0.48802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:45.738043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.00484
5-th percentile0.0162
Q10.0277925
median0.04111
Q30.0618325
95-th percentile0.1177395
Maximum0.48802
Range0.48318
Interquartile range (IQR)0.03404

Descriptive statistics

Standard deviation0.0396837004
Coefficient of variation (CV)0.771285492
Kurtosis15.34003079
Mean0.05145137671
Median Absolute Deviation (MAD)0.015575
Skewness3.121528744
Sum211.15645
Variance0.001574796077
MonotonicityNot monotonic
2022-05-10T12:13:45.885065image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024525
 
0.1%
0.025895
 
0.1%
0.032995
 
0.1%
0.035975
 
0.1%
0.022254
 
0.1%
0.041154
 
0.1%
0.0264
 
0.1%
0.026944
 
0.1%
0.030194
 
0.1%
0.036644
 
0.1%
Other values (3206)4060
98.9%
ValueCountFrequency (%)
0.004841
< 0.1%
0.005151
< 0.1%
0.005331
< 0.1%
0.005541
< 0.1%
0.00581
< 0.1%
0.006161
< 0.1%
0.006321
< 0.1%
0.006431
< 0.1%
0.006511
< 0.1%
0.006641
< 0.1%
ValueCountFrequency (%)
0.488021
< 0.1%
0.432831
< 0.1%
0.321141
< 0.1%
0.317211
< 0.1%
0.308841
< 0.1%
0.308741
< 0.1%
0.302931
< 0.1%
0.301541
< 0.1%
0.300921
< 0.1%
0.294891
< 0.1%

NHR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3937
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03191322563
Minimum0.000286
Maximum0.74826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:46.031092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.000286
5-th percentile0.00411055
Q10.01091175
median0.018577
Q30.0317965
95-th percentile0.088403
Maximum0.74826
Range0.747974
Interquartile range (IQR)0.02088475

Descriptive statistics

Standard deviation0.05776503695
Coefficient of variation (CV)1.810065758
Kurtosis55.07787487
Mean0.03191322563
Median Absolute Deviation (MAD)0.0093505
Skewness6.632003667
Sum130.971878
Variance0.003336799494
MonotonicityNot monotonic
2022-05-10T12:13:46.162151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0126193
 
0.1%
0.0074523
 
0.1%
0.0139653
 
0.1%
0.0048563
 
0.1%
0.0083113
 
0.1%
0.01552
 
< 0.1%
0.0140432
 
< 0.1%
0.0160572
 
< 0.1%
0.0181172
 
< 0.1%
0.0121742
 
< 0.1%
Other values (3927)4079
99.4%
ValueCountFrequency (%)
0.0002861
< 0.1%
0.0006261
< 0.1%
0.0006271
< 0.1%
0.0007741
< 0.1%
0.0008831
< 0.1%
0.0009931
< 0.1%
0.0011211
< 0.1%
0.0011431
< 0.1%
0.0011671
< 0.1%
0.0011791
< 0.1%
ValueCountFrequency (%)
0.748261
< 0.1%
0.724051
< 0.1%
0.708671
< 0.1%
0.706441
< 0.1%
0.704631
< 0.1%
0.642811
< 0.1%
0.609751
< 0.1%
0.591341
< 0.1%
0.575611
< 0.1%
0.559071
< 0.1%

HNR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3558
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.66957432
Minimum1.659
Maximum37.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:46.301169image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.659
5-th percentile14.8772
Q119.349
median21.9185
Q324.5065
95-th percentile28.0282
Maximum37.875
Range36.216
Interquartile range (IQR)5.1575

Descriptive statistics

Standard deviation4.30116761
Coefficient of variation (CV)0.1984887911
Kurtosis2.241763031
Mean21.66957432
Median Absolute Deviation (MAD)2.5775
Skewness-0.7670747763
Sum88931.933
Variance18.50004281
MonotonicityNot monotonic
2022-05-10T12:13:46.422204image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.9885
 
0.1%
21.5815
 
0.1%
22.1844
 
0.1%
25.4054
 
0.1%
20.3814
 
0.1%
22.6774
 
0.1%
17.5264
 
0.1%
21.1223
 
0.1%
19.9763
 
0.1%
22.2713
 
0.1%
Other values (3548)4065
99.0%
ValueCountFrequency (%)
1.6591
< 0.1%
1.7991
< 0.1%
1.8111
< 0.1%
1.8531
< 0.1%
1.9981
< 0.1%
2.9451
< 0.1%
2.9641
< 0.1%
3.0961
< 0.1%
3.1751
< 0.1%
3.2951
< 0.1%
ValueCountFrequency (%)
37.8751
< 0.1%
36.4481
< 0.1%
34.8361
< 0.1%
34.7951
< 0.1%
34.5551
< 0.1%
34.4571
< 0.1%
33.6291
< 0.1%
33.6041
< 0.1%
33.0681
< 0.1%
33.0511
< 0.1%

RPDE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3874
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5421752534
Minimum0.20929
Maximum0.96608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:46.550244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.20929
5-th percentile0.376119
Q10.471225
median0.542695
Q30.6150525
95-th percentile0.702247
Maximum0.96608
Range0.75679
Interquartile range (IQR)0.1438275

Descriptive statistics

Standard deviation0.1011614877
Coefficient of variation (CV)0.1865844799
Kurtosis-0.08007469239
Mean0.5421752534
Median Absolute Deviation (MAD)0.07202
Skewness-0.03264904762
Sum2225.08724
Variance0.01023364659
MonotonicityNot monotonic
2022-05-10T12:13:46.676267image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69753
 
0.1%
0.499073
 
0.1%
0.565573
 
0.1%
0.524723
 
0.1%
0.531013
 
0.1%
0.4993
 
0.1%
0.535153
 
0.1%
0.660573
 
0.1%
0.489072
 
< 0.1%
0.597692
 
< 0.1%
Other values (3864)4076
99.3%
ValueCountFrequency (%)
0.209291
< 0.1%
0.216221
< 0.1%
0.246631
< 0.1%
0.248711
< 0.1%
0.249331
< 0.1%
0.249341
< 0.1%
0.250751
< 0.1%
0.258781
< 0.1%
0.265891
< 0.1%
0.26911
< 0.1%
ValueCountFrequency (%)
0.966081
< 0.1%
0.947921
< 0.1%
0.935071
< 0.1%
0.926151
< 0.1%
0.922361
< 0.1%
0.915591
< 0.1%
0.869121
< 0.1%
0.865631
< 0.1%
0.849261
< 0.1%
0.831481
< 0.1%

DFA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3811
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6541436647
Minimum0.51404
Maximum0.8656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:46.813303image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.51404
5-th percentile0.550929
Q10.5969275
median0.644965
Q30.71229
95-th percentile0.774777
Maximum0.8656
Range0.35156
Interquartile range (IQR)0.1153625

Descriptive statistics

Standard deviation0.07091910321
Coefficient of variation (CV)0.1084151801
Kurtosis-0.8732350656
Mean0.6541436647
Median Absolute Deviation (MAD)0.056135
Skewness0.2824988762
Sum2684.6056
Variance0.005029519201
MonotonicityNot monotonic
2022-05-10T12:13:46.942320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.638343
 
0.1%
0.648453
 
0.1%
0.717493
 
0.1%
0.589213
 
0.1%
0.638713
 
0.1%
0.59393
 
0.1%
0.579653
 
0.1%
0.737433
 
0.1%
0.726483
 
0.1%
0.589743
 
0.1%
Other values (3801)4074
99.3%
ValueCountFrequency (%)
0.514041
< 0.1%
0.514681
< 0.1%
0.516871
< 0.1%
0.519711
< 0.1%
0.520921
< 0.1%
0.521141
< 0.1%
0.523011
< 0.1%
0.523341
< 0.1%
0.524471
< 0.1%
0.524721
< 0.1%
ValueCountFrequency (%)
0.86561
< 0.1%
0.85921
< 0.1%
0.840831
< 0.1%
0.836271
< 0.1%
0.82861
< 0.1%
0.822981
< 0.1%
0.8221
< 0.1%
0.821091
< 0.1%
0.819011
< 0.1%
0.817581
< 0.1%

PPE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3484
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2204961908
Minimum0.021983
Maximum0.73173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2022-05-10T12:13:47.073344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.021983
5-th percentile0.09978
Q10.1548225
median0.20618
Q30.26677
95-th percentile0.3961165
Maximum0.73173
Range0.709747
Interquartile range (IQR)0.1119475

Descriptive statistics

Standard deviation0.09267081876
Coefficient of variation (CV)0.4202830825
Kurtosis1.900643567
Mean0.2204961908
Median Absolute Deviation (MAD)0.05457
Skewness1.081201061
Sum904.916367
Variance0.008587880649
MonotonicityNot monotonic
2022-05-10T12:13:47.205380image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08654112
 
0.3%
0.13869
 
0.2%
0.0803588
 
0.2%
0.133998
 
0.2%
0.134558
 
0.2%
0.103128
 
0.2%
0.105628
 
0.2%
0.0978947
 
0.2%
0.0923777
 
0.2%
0.132036
 
0.1%
Other values (3474)4023
98.0%
ValueCountFrequency (%)
0.0219831
< 0.1%
0.0263361
< 0.1%
0.027611
< 0.1%
0.0282771
< 0.1%
0.0328581
< 0.1%
0.0331661
< 0.1%
0.0333621
< 0.1%
0.0351551
< 0.1%
0.0376311
< 0.1%
0.0378032
< 0.1%
ValueCountFrequency (%)
0.731731
< 0.1%
0.731521
< 0.1%
0.690561
< 0.1%
0.67791
< 0.1%
0.642351
< 0.1%
0.61891
< 0.1%
0.614451
< 0.1%
0.603331
< 0.1%
0.603121
< 0.1%
0.599571
< 0.1%

Interactions

2022-05-10T12:13:35.812344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:35.389195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:38.088619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:41.983137image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:44.539288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:47.837957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:50.536735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:53.048470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:55.652850image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:58.480372image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:01.303805image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:04.097563image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:06.926170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:10.469611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:13.372200image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:16.322269image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:19.256767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:21.955848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:24.659566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:27.369865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:30.225224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:33.128889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:35.937747image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:35.514593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:38.260903image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:42.090797image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:44.649055image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:47.947722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:50.646998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:53.174008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:55.778515image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:58.605767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:01.416358image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:04.207340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:07.049502image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:10.594518image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:13.513246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:16.448072image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:19.366587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:22.081267image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:24.779852image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:27.480046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:30.350628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:33.254602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:36.126090image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:35.734384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:38.512174image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:42.278467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:44.837986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:48.152042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:50.835409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:53.362511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:55.982451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:58.809712image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:01.618595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:04.396161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:07.253818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:10.798317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:13.732303image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:16.667634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:19.570571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:22.285175image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:24.983809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:27.668587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:30.538993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:33.443128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:36.220237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:35.844143image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:38.684855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:42.372797image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:44.932138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:48.262025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:50.929600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:53.456656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:56.108508image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:58.919892image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:01.728392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:04.522594image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:07.363591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:10.923729image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:13.842489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:16.777412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:19.680333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:22.394964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:25.093844image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:27.778382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:30.664612image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:33.552913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:13:36.330016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:35.953934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:38.841517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:42.467619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:45.026006image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:48.372133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:51.039268image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T12:12:53.566707image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-10T12:13:48.058240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-10T12:13:39.611101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-10T12:13:40.254491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexsubject#agesextest_timemotor_UPDRStotal_UPDRSJitter(%)Jitter(Abs)Jitter:RAPJitter:PPQ5Jitter:DDPShimmerShimmer(dB)Shimmer:APQ3Shimmer:APQ5Shimmer:APQ11Shimmer:DDANHRHNRRPDEDFAPPE
0505537561175.8237.6648.530.010.000.000.000.010.020.220.010.020.020.040.0421.670.490.600.18
114261058017.3212.0019.000.010.000.010.010.020.040.470.020.020.040.060.0422.590.590.740.33
221451665089.478.0118.030.010.000.000.000.010.040.340.020.020.030.060.0317.420.670.680.31
335272649059.8426.3333.320.010.000.010.010.020.060.580.030.040.060.100.0613.850.710.720.47
42142580115.8014.2216.400.010.000.000.000.010.040.360.020.030.030.070.0120.210.460.800.20
516141155014.5217.8920.180.000.000.000.000.000.010.130.010.010.010.020.0027.690.450.580.08
6529139660113.8832.0142.750.000.000.000.000.000.020.180.010.010.020.030.0026.420.500.700.13
717171262098.3615.0021.190.010.000.000.000.010.030.240.010.020.030.040.0220.940.540.560.17
827002067067.8910.7716.770.010.000.000.000.010.030.280.020.020.020.050.0125.160.480.800.22
945133366144.3724.5428.930.000.000.000.000.010.040.330.020.020.030.060.0221.210.500.530.21

Last rows

df_indexsubject#agesextest_timemotor_UPDRStotal_UPDRSJitter(%)Jitter(Abs)Jitter:RAPJitter:PPQ5Jitter:DDPShimmerShimmer(dB)Shimmer:APQ3Shimmer:APQ5Shimmer:APQ11Shimmer:DDANHRHNRRPDEDFAPPE
409444263366158.4125.0329.540.010.000.000.000.010.050.420.030.030.030.080.0516.350.620.580.30
4095534039660119.3932.2542.940.000.000.000.000.000.010.120.010.010.010.020.0027.770.450.600.07
4096466474044.6913.4019.830.010.000.000.010.010.090.890.050.060.080.150.0617.830.630.590.24
4097574142610115.6424.4035.400.010.000.000.000.010.030.340.010.020.030.040.0522.590.510.580.24
409830922359151.4213.5724.200.010.000.000.000.010.020.200.010.010.020.030.0222.740.410.680.19
409937722874197.3230.3935.220.010.000.000.000.010.030.230.010.020.020.040.0220.290.610.730.27
4100519738670160.7419.5826.860.000.000.000.000.010.020.180.010.010.010.030.0323.120.480.650.20
410152323867056.7619.8726.740.000.000.000.000.010.020.180.010.010.010.030.0222.950.600.610.28
410253963966012.5925.8335.110.000.000.000.000.000.020.210.010.010.020.030.0124.880.480.580.18
41038606630180.3429.9745.960.010.000.000.000.010.040.390.030.030.030.080.0318.860.550.650.24